Instructions to use WMSD/World-Model-Self-Distillation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use WMSD/World-Model-Self-Distillation with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("WMSD/World-Model-Self-Distillation", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
File size: 377 Bytes
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license: other
library_name: diffusers
tags:
- world-models
- self-distillation
- video-generation
- task-conditioned-video-generation
---
# World Model Self-Distillation
This repository will host the model weights for **World Model Self-Distillation: Training World Models to Solve General Tasks**.
Weights, loading instructions, and model details will be added later.
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